National Student Clearinghouse - Enrollment

Data Prep

Okay, lets check out the National Student Clearinghouse enrollment data. I will first import the entire dataset, clean it up, and then merge it with the master / Central graduate dataset.


PersonID EnrollmentBeginTimeID EnrollmentEndTimeID OPEID EnrollmentStatus OPEID.6
10025234 20120103 20120126 00235400 H 002354
11364693 20130122 20130517 00238881 F 002388
3090532 20090824 20091218 00238300 F 002383
11127041 20200601 20200819 00238200 L 002382
4465956 20100823 20101217 00299700 F 002997
9191392 20110110 20110513 00553400 L 005534
x
PersonID
EnrollmentBeginTimeID
EnrollmentEndTimeID
OPEID
EnrollmentStatus
OPEID.6


The dataset has 3,010,265 rows and 6 columns. Here are the columns and their definitions.

  • EnrollmentBeginTimeID: Begin date for the student’s period of attendance.
  • EnrollmentEndTimeID: End date for the student’s period of attendance.
  • OPEID: Office of Postsecondary Education (OPE)/FICE code of the college that the student attended (Foreign Key to IPEDSCharacteristics).
  • InstitutionName: Name of institution
  • EnrollmentStatus: The last enrollment status reported for the student. This field will have ‘N/A’ or “NULL” if the reporting college has not defined the student’s enrollment status as directory information. Here are the code definitions;
    • F: Full-time
    • Q: Three-quarter time
    • H: Half-time
    • L: Less than half-time
    • A: Leave of absence
    • W: Withdrawn
    • D: Deceased

Essentially, this dataset provides semester-based information - each observation is a semester/single period with the institutional information and beginning and end time of that single period (usually a semester). For example, if an individual attended South Dakota State University, attended in a “typical” fashion (fall and spring semesters), and graduated in 4 years, there would be 8 observations for that PersonID - one observation per semester.

There are a few pieces of information that I want to consolidate from this dataset;

  1. Did the PersonID in the master dataset attend a college - yes or no?
  2. Did the PersonID attend college during high school (PSEO), immediately after graduation or wait?
  3. Did the PersonID attend multiple colleges - yes or no?
  4. What type of college did the PersonID attend first?
  5. Did the PersonID attend a public, private not-for-profit, or private or, for-profit college(s)?
  6. At any point during their college career, did the PersonID attend a college(s) inside or outside of the Southwest Region or outside the EDR of their high school?

I was going to include the length of time that a PersonID attended college, but due to differences in how institutions report enrollment periods, it didn’t seem like it would be a great indicator.

The pieces of information missing in the original dataset is location and type of institution attended.To do this we will need to join the original dataset with an IPEDS dataset using opeid.

The IPEDS data and the NSC data will only match using the first 6 digits of the IPEDS OPEID values. So I will change that in the NSC data. Then I can join.


PersonID EnrollmentBeginTimeID EnrollmentEndTimeID OPEID EnrollmentStatus OPEID.6 Unitid City State FIPS GeographicRegion CountyCode InstitutionSector InstitutionName
10025234 20120103 20120126 00235400 H 002354 173665 Saint Paul MN 27 4 27123 2 Hamline University
11364693 20130122 20130517 00238881 F 002388 174233 Duluth MN 27 4 27137 1 University of Minnesota-Duluth
3090532 20090824 20091218 00238300 F 002383 174862 Saint Bonifacius MN 27 4 27019 2 Crown College
11127041 20200601 20200819 00238200 L 002382 174844 Northfield MN 27 4 27131 2 St Olaf College
4465956 20100823 20101217 00299700 F 002997 200332 Fargo ND 38 4 38017 1 North Dakota State University-Main Campus
9191392 20110110 20110513 00553400 L 005534 174756 Saint Cloud MN 27 4 27145 4 St Cloud Technical and Community College
x
PersonID
EnrollmentBeginTimeID
EnrollmentEndTimeID
OPEID
EnrollmentStatus
OPEID.6
Unitid
City
State
FIPS
GeographicRegion
CountyCode
InstitutionSector
InstitutionName


After joining these we now have 3,009,843 rows and 14 columns. This is a few less than the original enrollment document due to a few OPEIDs not aligning. But overall, the data now has the institution sector that the individual attended as well as location.

First, we will create a dataset with a column confirming their post-secondary attendance for each unique PersonID in the nsc.enrollment.ipeds.


PersonID attended.ps
10025234 Yes
11364693 Yes
3090532 Yes
11127041 Yes
4465956 Yes
9191392 Yes
x
PersonID
attended.ps


There are 388,959 rows and 2 columns in the dataset. The columns provide the unique PersonID along with a newly created column confirming that they attended post-secondary education.

Next we will extract the first month and year each PersonID attended a post-secondary institution which will help us figure out how long after high school graduation did they wait until they attended a post-secondary institution.


PersonID first.attend.ps
3090532 20090824
6790768 20080825
9172836 20060906
1597967 20060905
5583982 20100823
5456903 20230109
x
PersonID
first.attend.ps


This dataset has 397,372 rows and 2 columns. Essentially, this dataset provides each unique PersonID with the earliest begin time for post-secondary education.

Next we will determine how many different institiutions the PersonID attended during their post-secondary career.


PersonID n.institutions
14 1
98 1
141 1
198 2
208 1
227 2
x
PersonID
n.institutions


As expected, we have 397,372 rows and 2 columns. Each PersonID in the dataset has the number of unique post-secondary institutions they attended.

Next, we will create a dataset indicating the type of college the PersonID attended first. I will use the IPEDS sector data. Here are the definitions of the institution sector;

  • 0 - Administrative Unit
  • 1 - Public, 4-year or above
  • 2 - Private not-for-profit, 4-year or above
  • 3 - Private for-profit, 4-year or above
  • 4 - Public, 2-year
  • 5 - Private not-for-profit, 2-year
  • 6 - Private for-profit, 2-year
  • 7 - Public, less-than 2-year
  • 8 - Private not-for-profit, less-than 2-year
  • 9 - Private for-profit, less-than 2-year
  • 99 - Sector unknown (not active)


PersonID first.InstitutionSector
3090532 2
6790768 4
9172836 2
1597967 1
5583982 4
5456903 4
x
PersonID
first.InstitutionSector


As expected, we have 397,372 rows and 2 columns. This dataset now provides the sector of the first post-secondary institution they attended immediately after college.

Next we will determine thy type(s) of college(s) they attended during their post-secondary career. To do this we will first create a dataset with columns for each institution sector and a confirmation indicator on whether the PersonID attended that particular sector at some point in their career. I will then create a new category indicating that attended more than one type of institution sector. Once completed, I will be left with a dataset that has a column for each unique PersonID and what sector they attended, as well as a newly created code for “attended more than 1 type of sector”.


PersonID ps.sector.1 ps.sector.2 ps.sector.3 ps.sector.4 ps.sector.5 ps.sector.6 ps.sector.7 ps.sector.8 ps.sector.9
14 1 0 0 0 0 0 0 0 0
88 0 0 0 0 0 0 0 0 0
141 0 0 0 1 0 0 0 0 0
198 1 1 0 0 0 0 0 0 0
628 0 0 0 0 0 0 0 0 0
1004 0 0 0 0 0 0 0 0 0
x
PersonID
ps.sector.1
ps.sector.2
ps.sector.3
ps.sector.4
ps.sector.5
ps.sector.6
ps.sector.7
ps.sector.8
ps.sector.9


As expected, we have 137,622 rows and 10 columns. This dataset provides each unique PersonID with the institution sector they attended using the codes listed above. For PersonID’s that attended multiple sectors, they were coded as “10”.

Next we will determine whether they attended a post-secondary institution inside or outside of the planning region, outside their EDR, or outside of Minnesota. Since many of the PersonID in the dataset have attended multiple institutions, we will categorize it in the following way in order to capture the combinations of attendance;

  • Attended inside region only (planning region, EDR, RUCA, State)
  • Attended inside and outside region (planning region, EDR, RUCA, state)
  • Attended outside region only (planning region, EDR, RUCA, state)

In order to do this we will need to combine our planning region and EDR joining documents with the nsc.enrollment.ipeds dataset. I need to remember that the way I arranged the regions are going to be different than the official planning region. We will also need to join the master dataset with it to determine the location of the PersonID’s high school graduation location. Lastly, we will need to join up the RUCA categories for counties outside of Minnesota. Then we can start the categorization process.


PersonID InstitutionName CountyCode ps.Dem_Desc grad.edr grad.ruca grad.pr ps.countyfp ps.statefp ps.edr ps.pr
4465956 North Dakota State University-Main Campus 38017 Urban/town/rural mix EDR 7W- Central Urban/town/rural mix Central 017 38 NA NA
9191392 St Cloud Technical and Community College 27145 Urban/town/rural mix EDR 7W- Central Urban/town/rural mix Central 145 27 EDR 7W- Central Central
6790768 Anoka-Ramsey Community College 27003 Entirely urban EDR 7E- East Central Urban/town/rural mix Central 003 27 EDR 11 - 7 County Twin Cities Seven County Mpls-St Paul
3814355 Minneapolis Community and Technical College 27053 Entirely urban EDR 5 - North Central Town/rural mix Central 053 27 EDR 11 - 7 County Twin Cities Seven County Mpls-St Paul
10425144 Saint Cloud State University 27145 Urban/town/rural mix EDR 7E- East Central Urban/town/rural mix Central 145 27 EDR 7W- Central Central
6744901 North Dakota State University-Main Campus 38017 Urban/town/rural mix EDR 5 - North Central Town/rural mix Central 017 38 NA NA
x
PersonID
InstitutionName
CountyCode
ps.Dem_Desc
grad.edr
grad.ruca
grad.pr
ps.countyfp
ps.statefp
ps.edr
ps.pr


This joined dataset gives us 686,845 rows and 11 columns. The columns beginning with “ps” are the ruca category and regions of the post-secondary institution attended. The columns beginning with “grad” are the ruca category and regions of the highschool from which the PersonID graduated.

One thing that’s important is to realize that joining the nsc.enrollment dataset with the Central graduates dataset means we will have some NAs. Some of the students listed in the nsc.enrollment dataset aren’t in the Central graduates dataset since they may have graduated before 2008 or didn’t actually meet the criteria in the Central graduates dataset. Therefore, as we move forward, I will need to make sure to remove those NAs.

From here we can start creating new columns beginning with the RUCA category. to create this column we will gather each PersonID to examine whether or they attended a post-secondary institution in the same RUCA category, or if they attended multiple post-secondary insitutions with one institution in the same category and another not the same.


PersonID ps.in.same.ruca
14 Outside RUCA
141 Outside RUCA
198 In same RUCA
1103 Outside RUCA
1104 In same RUCA
1387 In same RUCA
x
PersonID
ps.in.same.ruca


So there are fewer observations in this dataset than previous subsets. Why? This is due to dropping and PersonID that wasn’t in the Central graduate dataset. I did not do that for the previous subsets. However, those previous subsets will be filtered down once we join it with the master dataset.

This dataset provides the PersonID and whether they attended a post secondary institution in a location with the same, outside, or both outside and inside (if attended multiple post-secondary institutions) RUCA categories of their high school from which they graduated. There are 95,356 rows and 2 columns.

Up next we will create a dataset indicating whether a PersonID that graduated from a Central MN high school attended a post-secondary insitution in their high school’s EDR.


PersonID ps.in.same.edr
14 Outside EDR
141 Outside EDR
198 Outside EDR
1103 Outside EDR
1104 Inside and outside same EDR
1387 In same EDR
x
PersonID
ps.in.same.edr


As expected there are 95,356 rows and 2 columns.

Next we will determine which Central MN graduates attended a post-secondary institution in the same project region (Central project region).


PersonID ps.in.same.pr
14 In same PR
141 Outside PR
198 Outside PR
1103 Outside PR
1104 Inside and outside same PR
1387 In same PR
x
PersonID
ps.in.same.pr


As expected, there are 95,356 rows and 2 columns. This dataset provides whether the post-secondary institution(s) attended were in the same planning region as the high school from which they graduates, outside of the planning region, or both (attended multiple institutions).

Next we want to see how many of the students leave the state to attend post-secondary education.


PersonID ps.in.MN
14 In MN
141 In MN
198 Inside and outside MN
1103 In MN
1104 In MN
1387 In MN
x
PersonID
ps.in.MN


As expected we have 95,356 rows and 2 columns. This dataset provides each distinct PersonID with whether they attended post secondary institutions inside MN, outside MN, or both.

Okay, now it’s time to join all of these with the master dataset. I will also create a new column that confirms whether they attended a college immediately (within the first year) after graduating high school.


PersonID K12OrganizationID OrganizationName county.name countyfp Dem_Desc edr grad.year grad.year.covid Gender LimitedEnglishProficiencyIndicator HomelessIndicator economic.status pseo.participant SpecialEdStatus non.english.home RaceEthnicity n.years.attended ACTCompositeScore ap.exam total.cte.courses.taken ACTE-SPED Agriculture, Food, and Natural Resources Architecture & Construction Arts, A/V Technology & Communication Business, Management & Administrative Diversified Education & Training Finance Government & Public Administration Health Science Hospitality & Tourism Human Services Information Technology Law, Public Safety & Security Manufacturing Marketing Program Area (Diversified) Science, Technology, Engineering, & Mathematics STEM Transportation, Distribution & Logistics Work Experience-Handicapped Work Experience-Handicapped (16-20+ on IEP) Work Experience/Career Exploration (age 14-15 on I Work-Experience-Disadvantaged Youth Apprenticeship cte.achievement english.learner MCA.M MCA.R MCA.S sat.taken attended.ps first.attend.ps attended.ps.first.year attended.ps.years.hsgrad attended.ps.within.first.year.hsgrad n.institutions first.InstitutionSector ps.sector.1 ps.sector.2 ps.sector.3 ps.sector.4 ps.sector.5 ps.sector.6 ps.sector.7 ps.sector.8 ps.sector.9 ps.in.same.ruca ps.in.same.edr ps.in.same.pr ps.in.MN
5356619 103290 LITTLE FALLS SENIOR HIGH Morrison 097 Town/rural mix EDR 5 - North Central 2013 Pre-covid grad F N N 0 1 0 0 White 3 21 0 5 0 1 0 1 0 0 0 0 0 1 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 CTE Participant 0 3 4 2 0 Yes 20130826 2013 0 Yes 4 1 1 0 0 1 0 0 0 0 0 Inside and outside same RUCA Outside EDR Inside and outside same PR Inside and outside MN
1506121 201946 PINE CITY AREA LEARNING CENTER Pine 115 Urban/town/rural mix EDR 7E- East Central 2022 Post-covid grad F N N 1 0 0 0 Unknown 3 NA 0 4 0 0 0 0 0 1 1 0 0 1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 CTE Concentrator or Completor 0 4 3 3 0 No NA NA NA NA 0 NA 0 0 0 0 0 0 0 0 0 NA NA NA NA
1456500 44383 EAGLE VALLEY SECONDARY Todd 153 Town/rural mix EDR 5 - North Central 2008 Pre-covid grad M N N 0 NA 0 0 Hispanic 1 NA 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 CTE Participant 0 NA NA NA 0 No NA NA NA NA 0 NA 0 0 0 0 0 0 0 0 0 NA NA NA NA
10870895 100018 SPECIAL EDUCATION FACILITIES Stearns 145 Urban/town/rural mix EDR 7W- Central 2015 Pre-covid grad M N N 1 0 1 1 Hispanic 6 NA 0 14 0 0 0 3 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 4 0 5 0 0 0 CTE Participant 1 1 1 1 0 No NA NA NA NA 0 NA 0 0 0 0 0 0 0 0 0 NA NA NA NA
1877865 160881 MONTICELLO SENIOR HIGH Wright 171 Urban/town/rural mix EDR 7W- Central 2016 Pre-covid grad F N N 1 0 0 0 White 3 14 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 No CTE 0 2 3 2 0 Yes 20170117 2017 1 Yes 1 4 0 0 0 1 0 0 0 0 0 In same RUCA Outside EDR Outside PR Outside MN
1323083 160874 MONTICELLO SENIOR HIGH Wright 171 Urban/town/rural mix EDR 7W- Central 2009 Pre-covid grad F N N 0 NA 0 0 White 3 23 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 No CTE 0 3 4 NA 0 Yes 20100601 2010 1 Yes 4 4 0 0 0 1 0 0 0 0 0 Outside RUCA Outside EDR Outside PR In MN
x
PersonID
K12OrganizationID
OrganizationName
county.name
countyfp
Dem_Desc
edr
grad.year
grad.year.covid
Gender
LimitedEnglishProficiencyIndicator
HomelessIndicator
economic.status
pseo.participant
SpecialEdStatus
non.english.home
RaceEthnicity
n.years.attended
ACTCompositeScore
ap.exam
total.cte.courses.taken
ACTE-SPED
Agriculture, Food, and Natural Resources
Architecture & Construction
Arts, A/V Technology & Communication
Business, Management & Administrative
Diversified
Education & Training
Finance
Government & Public Administration
Health Science
Hospitality & Tourism
Human Services
Information Technology
Law, Public Safety & Security
Manufacturing
Marketing
Program Area (Diversified)
Science, Technology, Engineering, & Mathematics
STEM
Transportation, Distribution & Logistics
Work Experience-Handicapped
Work Experience-Handicapped (16-20+ on IEP)
Work Experience/Career Exploration (age 14-15 on I
Work-Experience-Disadvantaged
Youth Apprenticeship
cte.achievement
english.learner
MCA.M
MCA.R
MCA.S
sat.taken
attended.ps
first.attend.ps
attended.ps.first.year
attended.ps.years.hsgrad
attended.ps.within.first.year.hsgrad
n.institutions
first.InstitutionSector
ps.sector.1
ps.sector.2
ps.sector.3
ps.sector.4
ps.sector.5
ps.sector.6
ps.sector.7
ps.sector.8
ps.sector.9
ps.in.same.ruca
ps.in.same.edr
ps.in.same.pr
ps.in.MN


As expected, we have the same number of rows as the original master dataset - 137,622 rows. Here are the explanations of all the new columns added to the master dataset.

  • attended.ps - this is a confirmation that the PersonID attended a post-secondary institution at some point after graduating high school.
  • attended.ps.years.hsgrad - the number of years after graduation that a PersonID attended a post-secondary education. In some cases, the value is negative indicating that the PersonID attended a post-secondary institution before graduating high school.
  • attended.ps.within.first.year.hsgrad - did the PersonID attend a post-secondary education institution within 1 year or less from graduating high school.
  • n.institutions - how many post-secondary institutions the PersonID attended
  • first.InstitutionSector - the sector of the first post-secondary institution attended.
  • InstitutionSector - The sector of the post-secondary institution attended (could be multiple)
  • ps.in.same.ruca - did the PersonID attend a post-secondary institution in the same RUCA category as their high school
  • ps.in.same.edr - did the PersonID attend a post-secondary institution in the same economic development region as high school
  • ps.in.same.pr - did the PersonID attend a post-secondary institution in the same planning region as high school
  • ps.in.MN = did the PersonID attend a post-secondary institution in Minnesota.


Summary of students attending post-secondary

Lets summarize the percentage of students that attended a post-secondary institution and compare across RUCA categories.

Below is the percentage of students that attended a post-secondary institution from the entire dataset. 67% of the PersonIDs in the dataset attended a post-secondary institution at some point between 2007 and 2024.



Now lets check to see if this percentage is statistically significantly different by RUCA group.

The crosstabs do inidcate that there is a statistically significant difference in the percentage of graduates that attend a post-secondary institution by high school RUCA category. The town/rural mix graduates attend post-secondary at a significantly lower rate than entirely urban and urban/town/rural mix graduates - 63.7%, compared to 70.7% and 67.7%, respectively.



 
   Cell Contents
|-------------------------|
|                       N |
|              Expected N |
|           N / Row Total |
|-------------------------|

 
Total Observations in Table:  137622 

 
                     | master.9$attended.ps 
   master.9$Dem_Desc |        No |       Yes | Row Total | 
---------------------|-----------|-----------|-----------|
      Entirely urban |      7509 |     18137 |     25646 | 
                     |  8350.948 | 17295.052 |           | 
                     |     0.293 |     0.707 |     0.186 | 
---------------------|-----------|-----------|-----------|
      Town/rural mix |     10590 |     18562 |     29152 | 
                     |  9492.585 | 19659.415 |           | 
                     |     0.363 |     0.637 |     0.212 | 
---------------------|-----------|-----------|-----------|
Urban/town/rural mix |     26714 |     56110 |     82824 | 
                     | 26969.466 | 55854.534 |           | 
                     |     0.323 |     0.677 |     0.602 | 
---------------------|-----------|-----------|-----------|
        Column Total |     44813 |     92809 |    137622 | 
---------------------|-----------|-----------|-----------|

 
Statistics for All Table Factors


Pearson's Chi-squared test 
------------------------------------------------------------
Chi^2 =  317.59     d.f. =  2     p =  1.086958e-69


Summary of years between graduation and attending post-secondary

Now lets take a look at the number of years between high school graduation and attending post-secondary. We will begin with the total number of students before diving into differences across RUCA categories and regions.

A huge majority of students that attended a post secondary institution waited less than 1 year after graduating high school (85%)



Next lets check to see if there are any differences in the numbers of years between high school and attending post secondary by RUCA category.

The ANOVA table indicates there are significant differences between the means across RUCA categories with a very low p-value. However, in the TukeyHSD test, it only shows that the statistically significant difference exists between urban/town/rural mix and town/rural mix districts.

It says that graduates from urban/town/rural districts wait to attend post-secondary institution .04 years less longer than town/rural mix districts.


               Df Sum Sq Mean Sq F value   Pr(>F)    
Dem_Desc        2     21  10.293   10.01 4.51e-05 ***
Residuals   92806  95458   1.029                     
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
  Tukey multiple comparisons of means
    95% family-wise confidence level

Fit: aov(formula = attended.ps.years.hsgrad ~ Dem_Desc, data = years.between.grad.ps.anova.ruca)

$Dem_Desc
                                           diff           lwr          upr
Town/rural mix-Entirely urban        0.02575178  0.0009346284  0.050568924
Urban/town/rural mix-Entirely urban -0.01254068 -0.0328435021  0.007762146
Urban/town/rural mix-Town/rural mix -0.03829245 -0.0584188601 -0.018166048
                                        p adj
Town/rural mix-Entirely urban       0.0398295
Urban/town/rural mix-Entirely urban 0.3164947
Urban/town/rural mix-Town/rural mix 0.0000245


Summary of number of colleges attended

Next we will summarize the number of colleges attended by producing the summary statistics and distribution.

The table and distribution chart below show that a large majority (60.3%) of individuals attended only one post secondary institutions followed by 26.7% attending two.



I’m not sure it’s all that important knowing whether there are differences in the percentage of students and the number of institutions they’ve attended across RUCA categories or regions. So we will skip that analysis for now.


Summary of types of first college attended

I think this will be very interesting - we want to see what they breakdown is of students attending different types of colleges. We will start by summarizing the total dataset before looking at differences across RUCA categories and regions.

The chart below provides the percentage of students in the entire dataset that attended each institution sector. By far, a huge majority attend either a public 4-year (44%) or public 2-year (37.8%).



Next we will check to see if those percentages are significantly different by RUCA category of the graduates high school.

The crosstabs indicate that there is a significant difference in the percentage of students attending different institution sectors depending on the RUCA category of their high school. The p-value was near zero.

The primary difference is the graduates from town/rural mix districts attend 2-year colleges at a higher percentage and attend public, 4-year colleges at a lower level.



 
   Cell Contents
|-------------------------|
|                       N |
|              Expected N |
|           N / Row Total |
|-------------------------|

 
Total Observations in Table:  92334 

 
                                        | first.college.sector.ct.1.ruca$first.InstitutionSector 
first.college.sector.ct.1.ruca$Dem_Desc |         1 |         2 |         3 |         4 | Row Total | 
----------------------------------------|-----------|-----------|-----------|-----------|-----------|
                         Entirely urban |      8162 |      2939 |       343 |      6585 |     18029 | 
                                        |  7972.601 |  2893.341 |   314.561 |  6848.497 |           | 
                                        |     0.453 |     0.163 |     0.019 |     0.365 |     0.195 | 
----------------------------------------|-----------|-----------|-----------|-----------|-----------|
                         Town/rural mix |      6883 |      2526 |       253 |      8833 |     18495 | 
                                        |  8178.670 |  2968.126 |   322.692 |  7025.512 |           | 
                                        |     0.372 |     0.137 |     0.014 |     0.478 |     0.200 | 
----------------------------------------|-----------|-----------|-----------|-----------|-----------|
                   Urban/town/rural mix |     25786 |      9353 |      1015 |     19656 |     55810 | 
                                        | 24679.729 |  8956.534 |   973.747 | 21199.991 |           | 
                                        |     0.462 |     0.168 |     0.018 |     0.352 |     0.604 | 
----------------------------------------|-----------|-----------|-----------|-----------|-----------|
                           Column Total |     40831 |     14818 |      1611 |     35074 |     92334 | 
----------------------------------------|-----------|-----------|-----------|-----------|-----------|

 
Statistics for All Table Factors


Pearson's Chi-squared test 
------------------------------------------------------------
Chi^2 =  950.4556     d.f. =  6     p =  4.632226e-202


Summary of attending college with same RUCA category

Up next is determining how many students attended a post-secondary institution located in a county with the same RUCA category as their high school. First we will look at the percentages of the total dataset and then we will break it up.

The chart below shows that 41.1% of students graduating from a Central MN high school attended a post secondary institution that was located in a county with the same RUCA category as their high school.



Next, lets check to see if the type of RUCA category they graduated from is related to whether the college they attend is in the same or different RUCA category.

The crosstabs indicate that there is a relationship in the location of the individuals high school graduation and the RUCA category of their post-secondary institution(s). The p-value was 0.

In a bit of a surprise, the individuals that graduated from an entirely urban district had a significantly lower percentage that graduated from a post-secondary institution that was also entirely urban.



 
   Cell Contents
|-------------------------|
|                       N |
|              Expected N |
|           N / Row Total |
|-------------------------|

 
Total Observations in Table:  92794 

 
                              | ps.same.ruca.ct.ruca$ps.in.same.ruca 
ps.same.ruca.ct.ruca$Dem_Desc |                 In same RUCA | Inside and outside same RUCA |                 Outside RUCA |                    Row Total | 
------------------------------|------------------------------|------------------------------|------------------------------|------------------------------|
               Entirely urban |                         5202 |                         4491 |                         8441 |                        18134 | 
                              |                     7446.169 |                     4075.137 |                     6612.694 |                              | 
                              |                        0.287 |                        0.248 |                        0.465 |                        0.195 | 
------------------------------|------------------------------|------------------------------|------------------------------|------------------------------|
               Town/rural mix |                         6722 |                         3845 |                         7992 |                        18559 | 
                              |                     7620.682 |                     4170.645 |                     6767.673 |                              | 
                              |                        0.362 |                        0.207 |                        0.431 |                        0.200 | 
------------------------------|------------------------------|------------------------------|------------------------------|------------------------------|
         Urban/town/rural mix |                        26179 |                        12517 |                        17405 |                        56101 | 
                              |                    23036.149 |                    12607.218 |                    20457.633 |                              | 
                              |                        0.467 |                        0.223 |                        0.310 |                        0.605 | 
------------------------------|------------------------------|------------------------------|------------------------------|------------------------------|
                 Column Total |                        38103 |                        20853 |                        33838 |                        92794 | 
------------------------------|------------------------------|------------------------------|------------------------------|------------------------------|

 
Statistics for All Table Factors


Pearson's Chi-squared test 
------------------------------------------------------------
Chi^2 =  2462.127     d.f. =  4     p =  0


Summary of attending college in same planning region as high school

Now we want to see how many students attend a college that is located in the same planning region as their high school.

The chart below shows that nearly 64% of students graduating from Central MN High schools leave the region to attend post secondary education.



Summary of attending college in Minnesota

Now we want to see how many students stay or leave Minnesota to attend college.

The chart below shows that 71% of students graduating from Central MN high schools attend a post secondary institution in Minnesota.



Next, lets check to see if the RUCA category of their high school is related to whether they attend a college inside or outside Minnesota.

The crosstabs below indicate that there is a relationship between the RUCA category of a student’s high school and whether they attend a college inside or outside of Minnesota. The p-value was near zero.

The primary difference is that as districts become more urban, the percentage of graduates that attend a college in Minnesota declines - 68% for entirely urban, 71% for urban/town/rural mix and 73% for town/rural mix graduates.



 
   Cell Contents
|-------------------------|
|                       N |
|              Expected N |
|           N / Row Total |
|-------------------------|

 
Total Observations in Table:  92809 

 
                          | ps.in.MN.ct.ruca$ps.in.MN 
ps.in.MN.ct.ruca$Dem_Desc |                 In MN | Inside and outside MN |            Outside MN |             Row Total | 
--------------------------|-----------------------|-----------------------|-----------------------|-----------------------|
           Entirely urban |                 12286 |                  2614 |                  3237 |                 18137 | 
                          |             12844.558 |              2415.231 |              2877.211 |                       | 
                          |                 0.677 |                 0.144 |                 0.178 |                 0.195 | 
--------------------------|-----------------------|-----------------------|-----------------------|-----------------------|
           Town/rural mix |                 13490 |                  2449 |                  2623 |                 18562 | 
                          |             13145.542 |              2471.827 |              2944.632 |                       | 
                          |                 0.727 |                 0.132 |                 0.141 |                 0.200 | 
--------------------------|-----------------------|-----------------------|-----------------------|-----------------------|
     Urban/town/rural mix |                 39951 |                  7296 |                  8863 |                 56110 | 
                          |             39736.900 |              7471.942 |              8901.158 |                       | 
                          |                 0.712 |                 0.130 |                 0.158 |                 0.605 | 
--------------------------|-----------------------|-----------------------|-----------------------|-----------------------|
             Column Total |                 65727 |                 12359 |                 14723 |                 92809 | 
--------------------------|-----------------------|-----------------------|-----------------------|-----------------------|

 
Statistics for All Table Factors


Pearson's Chi-squared test 
------------------------------------------------------------
Chi^2 =  135.4662     d.f. =  4     p =  2.636668e-28


What states are students going to college

The last piece here is to provide a map of the United States to see where students are going for college.

Outside of Minnesota, Central MN graduates attend colleges in North Dakota at the highest rate - 7.8%. This is followed by Wisconsin at 5.3%.